Antarctic Whale Project: Single Species
Ben Weinstein
2016-08-17 18:37:58
1 Descriptive Statistics
1.1 Distance
1.2 Time
1.3 Velocity
1.4 Angles
2 Correlated random walk
Process Model
\[ d_{t} \sim T*d_{t-1} + Normal(0,\Sigma)\] \[ x_t = x_{t-1} + d_{t} \]
2.1 Parameters
For each individual:
\[\theta = \text{Mean turning angle}\] \[\gamma = \text{Move persistence} \]
For both behaviors process variance is: \[ \sigma_{latitude} = 0.1\] \[ \sigma_{longitude} = 0.1\]
2.2 Behavioral States
\[ \text{For each individual i}\] \[ Behavior_1 = \text{traveling}\] \[ Behavior_2 = \text{foraging}\]
\[ \alpha_{i,1,1} = \text{Probability of remaining traveling when traveling}\] \[\alpha_{i,2,1} = \text{Probability of switching from Foraging to traveling}\]
\[\begin{matrix} \alpha_{i,1,1} & 1-\alpha_{i,1,1} \\ \alpha_{i,2,1} & 1-\alpha_{i,2,1} \\ \end{matrix} \]
2.3 Environment
Behavioral states are a function of local environmental conditions. The first environmental condition is ocean depth. I then build a function for preferential foraging in shallow waters.
It generally follows the form, conditional on behavior at t -1:
\[Behavior_t \sim Multinomial([\phi_{traveling},\phi_{foraging}])\]
With the probability of switching states:
\[logit(\phi_{traveling}) = \alpha_{Behavior_{t-1}} + \beta_1 * Ocean_{y[t,]} + \beta_2 * Coast_{y[t,]}\]
\[logit(\phi_{foraging}) = \alpha_{Behavior_{t-1}} \]
Following Bestley in preferring to describe the switch into feeding, but no estimating the resumption of traveling.
2.4 Continious tracks
The transmitter will often go dark for 10 to 12 hours, due to weather, right in the middle of an otherwise good track. The model requires regular intervals to estimate the turning angles and temporal autocorrelation. As a track hits one of these walls, call it the end of a track, and begin a new track once the weather improves. We can remove any micro-tracks that are less than three days. Specify a duration, calculate the number of tracks and the number of removed points. Iteratively.
How did the filter change the extent of tracks?
## user system elapsed
## 197.631 1.976 6391.3252.5 Chains
2.5.1 Compare to priors
2.6 Parameter Summary
## Source: local data frame [12 x 5]
## Groups: parameter [?]
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## parameter par mean lower upper
## (fctr) (fctr) (dbl) (dbl) (dbl)
## 1 alpha_mu alpha_mu[1] 0.4994257493 -0.119244348 1.11470312
## 2 alpha_mu alpha_mu[2] -1.9759865331 -2.561190955 -1.44140957
## 3 beta2_mu beta2_mu[1] 0.0177734219 0.006279414 0.03012112
## 4 beta2_mu beta2_mu[2] 0.0000000000 0.000000000 0.00000000
## 5 beta3_mu beta3_mu[1] 0.0060298574 -0.001263055 0.01349180
## 6 beta3_mu beta3_mu[2] 0.0000000000 0.000000000 0.00000000
## 7 beta_mu beta_mu[1] -0.9159653731 -1.756814177 -0.10724389
## 8 beta_mu beta_mu[2] 0.0000000000 0.000000000 0.00000000
## 9 gamma gamma[1] 0.8794301200 0.835756888 0.91506098
## 10 gamma gamma[2] 0.0734836347 0.006255442 0.13873853
## 11 theta theta[1] 0.0004216938 -0.024421321 0.02432177
## 12 theta theta[2] 5.2415578174 2.862968098 5.98749529
2.7 Behavior and environment
2.7.1 Ocean Depth
2.7.2 Distance to Coast
3 Behavioral Prediction
3.0.1 Correlation in posterior switching and state
3.1 Spatial Prediction
3.1.1 Per Animal
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3.2 Log Odds of Foraging
3.2.1 Ocean Depth
3.2.2 Distance From Coast
3.2.3 Interaction
No estimate of uncertainty. 
3.3 Autocorrelation in behavior
3.4 Behavioral description
3.5 Predicted behavior duration
3.6 Location of Behavior
4 Environmental Prediction - Probability of Foraging
4.1 Bathymetry
4.2 Distance to coast
4.3 All variables
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5 Krill Fishery
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1759740 94.0 5750464 307.2 8985100 479.9
## Vcells 237448968 1811.6 465418839 3550.9 464037574 3540.4